Abstract
Ultrasonographic characteristics of skeletal muscles are related to their health status and functional capacity, but they still provide limited information on muscle composition during the inflammatory process. It has been demonstrated that an alteration in muscle composition or structure can have disparate effects on different ranges of ultrasonogram pixel intensities. Therefore, monitoring specific clusters or bands of pixel intensity values could help detect echotextural changes in skeletal muscles associated with neurogenic inflammation. Here we compare two methods of ultrasonographic image analysis, namely, the echointensity (EI) segmentation approach (EI banding method) and detection of selective pixel intensity ranges correlated with the expression of inflammatory regulators using an in-house developed computer algorithm (r-Algo). This study utilized an experimental model of neurogenic inflammation in segmentally linked myotomes (i.e., rectus femoris (RF) muscle) of rats subjected to lumbar facet injury. Our results show that there were no significant differences in RF echotextural variables for different EI bands (with 50- or 25-pixel intervals) between surgery and sham-operated rats, and no significant correlations among individual EI band pixel characteristics and protein expression of inflammatory regulators studied. However, mean numerical pixel values for the pixel intensity ranges identified with the proprietary r-Algo computer program correlated with protein expression of ERK1/2 and substance P (both 86-101-pixel ranges) and CaMKII (86-103-pixel range) in RF, and were greater (p < 0.05) in surgery rats compared with their sham-operated counterparts. Our findings indicate that computer-aided identification of specific pixel intensity ranges was critical for ultrasonographic detection of changes in the expression of inflammatory mediators in neurosegmentally-linked skeletal muscles of rats after facet injury.